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From "Ted Dunning (JIRA)" <>
Subject [jira] [Commented] (MAHOUT-1265) Add Multilayer Perceptron
Date Sun, 04 Aug 2013 04:46:04 GMT


Ted Dunning commented on MAHOUT-1265:

We have several efforts that are going to be helped by a parameter server implementation.
 Deep learning is one.  Other non-linear optimizations are likely to as well.

Is this MLP issue a good place to start with that?

> Add Multilayer Perceptron 
> --------------------------
>                 Key: MAHOUT-1265
>                 URL:
>             Project: Mahout
>          Issue Type: New Feature
>            Reporter: Yexi Jiang
>              Labels: machine_learning, neural_network
>         Attachments: mahout-1265.patch
> Design of multilayer perceptron
> 1. Motivation
> A multilayer perceptron (MLP) is a kind of feed forward artificial neural network, which
is a mathematical model inspired by the biological neural network. The multilayer perceptron
can be used for various machine learning tasks such as classification and regression. It is
helpful if it can be included in mahout.
> 2. API
> The design goal of API is to facilitate the usage of MLP for user, and make the implementation
detail user transparent.
> The following is an example code of how user uses the MLP.
> -------------------------------------
> //  set the parameters
> double learningRate = 0.5;
> double momentum = 0.1;
> int[] layerSizeArray = new int[] {2, 5, 1};
> String costFuncName = “SquaredError”;
> String squashingFuncName = “Sigmoid”;
> //  the location to store the model, if there is already an existing model at the specified
location, MLP will throw exception
> URI modelLocation = ...
> MultilayerPerceptron mlp = new MultiLayerPerceptron(layerSizeArray, modelLocation);
> mlp.setLearningRate(learningRate).setMomentum(momentum).setRegularization(...).setCostFunction(...).setSquashingFunction(...);
> //  the user can also load an existing model with given URI and update the model with
new training data, if there is no existing model at the specified location, an exception will
be thrown
> /*
> MultilayerPerceptron mlp = new MultiLayerPerceptron(learningRate, regularization, momentum,
squashingFuncName, costFuncName, modelLocation);
> */
> URI trainingDataLocation = …
> //  the detail of training is transparent to the user, it may running in a single machine
or in a distributed environment
> mlp.train(trainingDataLocation);
> //  user can also train the model with one training instance in stochastic gradient descent
> Vector trainingInstance = ...
> mlp.train(trainingInstance);
> //  prepare the input feature
> Vector inputFeature …
> //  the semantic meaning of the output result is defined by the user
> //  in general case, the dimension of output vector is 1 for regression and two-class
> //  the dimension of output vector is n for n-class classification (n > 2)
> Vector outputVector = mlp.output(inputFeature); 
> -------------------------------------
> 3. Methodology
> The output calculation can be easily implemented with feed-forward approach. Also, the
single machine training is straightforward. The following will describe how to train MLP in
distributed way with batch gradient descent. The workflow is illustrated as the below figure.
> For the distributed training, each training iteration is divided into two steps, the
weight update calculation step and the weight update step. The distributed MLP can only be
trained in batch-update approach.
> 3.1 The partial weight update calculation step:
> This step trains the MLP distributedly. Each task will get a copy of the MLP model, and
calculate the weight update with a partition of data.
> Suppose the training error is E(w) = ½ \sigma_{d \in D} cost(t_d, y_d), where D denotes
the training set, d denotes a training instance, t_d denotes the class label and y_d denotes
the output of the MLP. Also, suppose sigmoid function is used as the squashing function, 
> squared error is used as the cost function, 
> t_i denotes the target value for the ith dimension of the output layer, 
> o_i denotes the actual output for the ith dimension of the output layer, 
> l denotes the learning rate,
> w_{ij} denotes the weight between the jth neuron in previous layer and the ith neuron
in the next layer. 
> The weight of each edge is updated as 
> \Delta w_{ij} = l * 1 / m * \delta_j * o_i, 
> where \delta_j = - \sigma_{m} * o_j^{(m)} * (1 - o_j^{(m)}) * (t_j^{(m)} - o_j^{(m)})
for output layer, \delta = - \sigma_{m} * o_j^{(m)} * (1 - o_j^{(m)}) * \sigma_k \delta_k
* w_{jk} for hidden layer. 
> It is easy to know that \delta_j can be rewritten as 
> \delta_j = - \sigma_{i = 1}^k \sigma_{m_i} * o_j^{(m_i)} * (1 - o_j^{(m_i)}) * (t_j^{(m_i)}
- o_j^{(m_i)})
> The above equation indicates that the \delta_j can be divided into k parts.
> So for the implementation, each mapper can calculate part of \delta_j with given partition
of data, and then store the result into a specified location.
> 3.2 The model update step:
> After k parts of \delta_j been calculated, a separate program can be used to merge the
k parts of \delta_j into one to update the weight matrices.
> This program can load the results calculated in the weight update calculation step and
update the weight matrices. 

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